We describe methods to identify {tau} leptons produced in high energy p{bar p} collisions ({radical}s = 1.96 GeV) at the Tevatron, using the D0 detector. Different procedures used for discrimination against background particles misidentified as taus are also discussed. Finally, we present some physics results obtained by applying these methods to illustrate their performance

Galea, Cristina

Nijmegen, /Radboud U.

UNT Digital Library

FERMILAB-CONF-06-462-E D0 Note# 5304Tau Identification at DØ ∗Cristina Galea, for the DØ CollaborationRadboud Universiteit Nijmegen/NIKHEFNL - 6525 ED Nijmegen, The NetherlandsWe describe methods to identify τ leptons produced in high energypp¯ collisions (√s = 1.96 GeV) at the Tevatron, using the DØ detector.Different procedures used for discrimination against background particlesmisidentified as taus are also discussed. Finally, we present some physicsresults obtained by applying these methods to illustrate their performance.PACS numbers: 13.38.Dg, 13.85.Qk, 14.70.Hp1. IntroductionThe taus are elusive particles: with a mass of 1.78 GeV and a shortlifetime cτ of 87 µm, no detector in current or near future high energycollision experiments is ever close enough to detect them before they decay.Besides, about half of their energy is carried away invisibly by the tauneutrino, making their detection even harder. So why should one even try?There are many compelling reasons. The most straightforward one isof course the increased acceptance for channels with leptons: assuming thesame efficiency for any lepton identification, this would lead to increasefactors of 1.5, 2 and 3 for the single lepton channel, two lepton channel andthree lepton channel, respectively. Another reason is the fact that manyinteresting and yet undiscovered physical processes favor the production ofτ leptons compared to most other particles, except maybe the b quarks: thelargest coupling of the Higgs boson to leptons is to taus, the minimal SUSYmodels with large tanβ favor decays to taus as well, and obviously the 3rd∗ Presented at the conference “Physics at LHC”, 3-8 July 2006, Polish Academy ofArts and Sciences, Krakow, Poland(1)2generation of lepto-quarks can only be discovered in the tau channel, if theyexist.The DØ detector is a large general purpose detector for the study ofphenomena in high energy pp¯ collisions, now in operation at the FermilabTevatron Collider. For a complete description of the detector see [1].2. Tau TriggersThe selection of events containing taus in the final state may start onlinewith triggers specifically designed to favor hadronic tau decays. DØ has athree-tier triggering system and is using both single and di-tau triggers.The single hadronic tau (τh) triggers require a track and a calorimetertower at Level 1, followed by identification based on a Neural Network (NN)at Level 3 (see section 4 for a discussion on NN). These triggers are used incoincidence with missing transverse energy ( /ET ) triggers for the W → τντanalysis.The di-tau triggers may be µ + τh, e + τh or τh + τh. The analyses withthese triggers are at early stages in DØ. For the Z → ττ and H → ττresults presented here, single electron and single muon triggers were used.Due to bandwidth limitations, all τ triggers can add up to a maximumof 2 Hz to tape at instantaneous luminosities of 1032cm−2s−1.3. The DØ Tau ReconstructionWithin the DØ experiment a tau candidate is a collection of the followingobjects:• Calorimeter Cluster: reconstructed using the Simple Cone algo-rithm with cone size R = 0.5. A core cone of size Rcore = 0.3 isused for the calculation of isolation variables. The cluster should haverms < 0.25, with rms =√∑ni=1[(∆φi)2 + (∆ηi)2]ETi/ET , where i isthe index of the calorimeter tower associated with the τ -cluster.• Associated Tracks: all tracks in a R = 0.3 cone around the calorime-ter cluster, compatible with a τ decay ( invariant mass < 1.8 GeV ).At least one track with pT > 1.5 GeV is required.• EM sub-clusters: reconstructed with the Nearest Neighbor algo-rithm using a seed situated in the third layer of the electromagneticcalorimeter (EM3), where the showers are supposed to reach theirmaximum. The EM3 also has the highest granularity within the DØcalorimeter. The EM sub-cluster must have an energy E > 800 MeV.3The tau candidates always have a calorimeter cluster. They are thenclassified into three types, depending on the number of associated tracksand EM sub-clusters they posses:• type 1: only one associated track and no EM sub-clusters• type 2: only one associated track and at least one EM sub-cluster• type 3: at least two associated tracks and ≥ 0 EM sub-clusters4. Jet background reductionDue to the high background from jets (more than half of all jets willbe misidentified as taus, see Table 1 ), other selection criteria have to beapplied in order to distinguish between jets and taus. Three separate NeuralNetworks (NN1, NN2 and NN3), one for each τ -type, were trained using tausfrom Monte Carlo events as signal and jets back-to-back to non-isolatedmuons from data as background. All NNs presented in this and followingsections use a simple back propagation algorithm, with one node for eachinput variable, one intermediate layer having a number of hidden nodesequal to the number of input nodes, and a single output node.The input variables used can be classified as follows:• isolation variables : caliso = (EτT −EcoreT )/EcoreT ,trkiso =∑ptrkT /∑pτtrkT , em12isof = (EEM1 + EEM2)/Eτ• shower shape variables : rms, EM and hadronic fractions,profile = Eleading 2 towersT /EτT , prf3 = Eleading EM sub−clusterT /EEM3T• calorimeter - track correlation variables : EτT /(EτT +∑pτtrkT ),δα = angle between∑τtrk and∑EM sub-clusterswhere τtrk (trk) are tracks associated (unassociated) with the tau withinthe R < 0.5 cone and EEMi is the energy in ith layer of the EM calorimeter.Figure 1 shows the distribution of some of these input variables for type1 tau candidates. One can see that no particular distribution can be used toclearly distinguish between signal and background, but when used togetherwithin a NN they can be very effective in making this separation.The output of a Neural Network should be close to 0 for the backgroundand near 1 for the signal. Figure 2 shows the NN output for backgroundjets and for taus from Z → ττ Monte Carlo events for the three τ -types.The distributions are normalized with respect to each other such that thesum over the three τ -types is 1. As shown in Table 1, a cut placed onthe NN output variable at 0.9 reduces the jet background by a factor of4em12isof0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1110210310(a)caliso0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1100200300400500600700800900(b)profile0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 120406080100120140(c)trkiso0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1110210310(d)Fig. 1. Some NN input variables distributions for τ -type 1: (a) em12isof (b) caliso(c) profile (d) trkiso. The signal distributions are shown in (light) green, whilethe background ones are shown in (dark) blue.0 0.5 1-310-210 MCτjet data Run II Preliminary∅D0 0.5 1-310-210-110  MCτjet data Run II Preliminary∅D0 0.5 1-210-110  MCτjet data Run II Preliminary∅DFig. 2. NN output distributions for type 1 taus (NN1, left), type 2 taus (NN2,middle) and type 3 taus (NN3, right). The signal sample distributions (taus fromMonte Carlo) are shown in (light) green, while the background ones (jets back-to-back to non-isolated muons from data) are shown in (dark) blue.50 to sub-percent levels for all τ -types, while keeping the total efficiencyfor hadronic taus close to 70 %. The numbers in this table correspond tothe tau candidates with transverse energy between 20 and 40 GeV. Notethat excluding τ -type 3 would bring a factor of 3 increase in the signal tobackground ratio, with only a 24 % loss in efficiency.5τ -type 1 2 3 alljets 2 12 38 52τ 11 60 24 95NN > 0.9jets 0.06 0.24 0.80 1.1τ 7 44 16 67Table 1. Efficiencies (%) for tau identification, before and after the NN > 0.9 cut.The numbers include branching ratios and do not include τ → µνµντ decays.5. Electron background reductionWith their often isolated track corresponding to a calorimeter clusterand with most of the showering taking place in the EM layers, the electronsmake nice type 2 tau candidates. In fact, they are so similar to the realtaus, that a cut placed at 0.9 on the NN2 output will only reject about 2 %of the electrons. Virtually all electrons will be identified as type 2 taus byall the means we described above. To discriminate against them, a specialNeural Network (reffered to by NNe from now on) was designed using mostof the variables described in the previous section, but this time training theNN with electrons from a Monte Carlo sample as background. The resultsof the NNe and the output of NN2 shown in comparisson can be seen inFigure 3. For the transverse energy region of the tau candidate between 20and 40 GeV, a 0.9 cut on NN2 would let 98% of the electrons pass, while a0.5 cut on the NNe output will reduce the electron background to only 3.4%, with an efficiency loss for type 2 taus of only 14 % (from 44 % to 38 %).6. Muon background reductionDue to the limited coverage of the muon detectors (larger gaps at highη and at the bottom, needed for the calorimeter support and the readoutsystems) only 80 % of muons faking taus are removed by rejecting tracksreconstructed as muons. About 1.2 % of all muons can still fake tau can-didates of types 1 or 2. This can be reduced by a factor of two (from 0.4% to 0.2 % for type 1, and from 0.8 % to 0.4 % for type 2) by requiringRµ > 0.4, where Rµ is defined by Equation (1) . This variable exploits thefact that most taus deposit the greatest part of their energy in the innerlayers of the calorimeter, while the muons usually do so in the outer layers,namely in the coarse hadronic (CH) section.Rµ = EτT (1− fCH)ptrackT(1)60 0.2 0.4 0.6 0.8 1-410-310-210-1101Z e e MC MCτ τZ  Run II Preliminary∅D0 0.2 0.4 0.6 0.8 1-310-210-1101hNNetcEntries  6482Mean  0.8442RMS    0.3107Z e e MC MCτ τZ  Run II Preliminary∅DFig. 3. NN output distributions for τ -type 2: trained on jets as background (NN2,left) and trained on electrons as background (NNe, right). The signal sample distri-butions (taus from Monte Carlo) are shown in (light) green, while the backgroundones (electrons from Monte Carlo) are shown in (dark) red.where EτT is the ET of the calorimeter cluster, ptrackT is the pT of the associ-ated track and fCH is the fraction of ET deposited by the tau candidate inthe CH layers. Figure 4 shows the effect of the µ identification and Rµ cutson data events containing a muon and a tau candidate matched to a muon.0 50 100 150 200050100150200250)µ µOS( MCµ µZ )µ µSS() cutµ µOS( MC cutµ µZ ) cutµ µSS( Run II Preliminary∅D0 50 100 150 200050100150200250300350400450500)µ µOS( MCµ µZ )µ µSS() cutµ µOS( MC cutµ µZ ) cutµ µSS( Run II Preliminary∅DFig. 4. Invariant mass distributions using a µ and a τ candidate. The dots showdata events where the two particles have opposite sign (OS), before (filled) andafter (empty) the cuts to remove misidentified muons, for type 1 (left) and type 2(right) tau candidates. The distributions for same sign (SS) µ−τ pairs also shown.77. Testing the tau identification on dataIn order to test the effectiveness of the tau identification and backgroundreduction methods explained in the previous sections, we selected eventsexpected to have a large contribution from Z → ττ . Each event was requiredto have one and only one isolated muon with pµT > 12 GeV and |ηµ| < 1.7, atau candidate with ET > 10 GeV and |ητ | < 2.5, Rµ > 0.4 and |φµ − φτ | >2.7. The data was selected from a sample of about∫Ldt = 630 pb−1.0 0.5 1310410OS data MCτ τZ SS data Run II Preliminary∅D0 0.5 1310410OS data MCτ τZ  MCν µW SS data Run II Preliminary∅D0.8 0.85 0.9 0.95 1210310 OS data MCτ τZ µ µ MCν µW SS data Run II Preliminary∅D0.8 0.85 0.9 0.95 1210310OS data MCτ τZ  MCν µW SS data Run II Preliminary∅DFig. 5. NN output distributions for the sum of τ -type 1 and τ -type 2 (left) andfor τ -type 3 (right).Figure 5 shows the NN output distributions for events with opposite sign(OS) and same sign (SS) µ − τ pairs, before and after the NN > 0.8 cut.The excess of OS events at high NN output values is estimated to comefrom Z → τµτh/e events. This is illustrated by showing the contributionfrom a Z → ττ MC sample, normalized to the number of OS events fromdata after background subtraction. The backgrounds are estimated fromdata, by selecting same sign µ− τ pairs for QCD backgrounds (jets fakingtaus) or muons misidentified as taus for the Z → µµ background. High cutswere placed on the muon transverse momentum and missing transverse en-8ergy (pµT > 20 GeV and /ET > 20 GeV) for estimating the W → µν+jetbackground. We then rely on Monte Carlo to extrapolate all of these back-grounds into the signal region.After all cuts have been applied, there are close to 5000 taus abovean equivalent amount of background in this sample. Figure 6 shows theinvariant mass distributions of the µ - τ pairs after the NN > 0.8 cut.0 50 100 150 2000100200300400500600700OS data MCτ τZ µ µ MCν µW SS data Run II Preliminary∅D0 50 100 150 200050100150200250300350400OS data MCτ τZ  MCν µW SS data Run II Preliminary∅DFig. 6. Invariant mass distributions of the µ and all the tracks associated with theτ -candidate for the sum of τ -type 1 and τ -type 2 (left) and for τ -type 3 (right).8. Physics results using tau identificationUsing a method and selection criteria very similar to the ones shownin the previous sections, DØ measured the Z → ττ cross section with asample of 226 pb−1. The result (237 ± 15stat ± 18sys ± 15lum pb) based onthe Z → τµτh/e channel [2] is in very good agreement with the StandardModel expectations.Numerous searches are under way for interesting final states involvingtaus. Some limits have already been either published, presented at this andother conferences or submitted for publication. Examples include limitson 3rd generation lepto-quark production [3] , R-parity violating SUSYprocesses [4] or H → ττ production [5].9. Conclusions and outlookThe tau identification capabilities of the DØ experiment are now proven.Jet rejections of 99 % or better can be achieved while keeping for tausdecaying hadronically or to an electron efficiencies near 70 %. The number ofelectrons and muons misidentified as taus can be reduced to very low levels.This way, it seems promising that measurements with the tau channels at9DØ can ultimately achieve a few percent precision, making the τ leptonsan important handle in the search for new physics at the Tevatron. Thiswealth of experience will be valuable for all LHC experiments.REFERENCES[1] V.M. Abazov et al. (DØ Collaboration), Nucl. Inst. and Methods Phys. Res.A. 565, 463 (2006)[2] V.M. Abazov et al. (DØ Collaboration), Phys. Rev. D 71, 072004 (2005)[3] Elemer Nagy, these proceedings[4] V.M. Abazov et al. (DØ Collaboration), Phys. Lett. B 638, 441 (2006)[5] V. M. Abazov et al. (DØ Collaboration), Phys. Rev. Letters 97, 121802 (2006)

Tau identification at D0

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Tau identification at D0

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